Language is the operating system of consciousness, providing the code (words, grammar) to package the continuous stream of sensory data into discrete, transmissible units. Without language, the majority of data remains a personal, incommunicable experience. It is language that transforms raw sensory perception into a universally shared concept that can be conveyed, discussed, and used as a basis for building collective knowledge. Thus, any analysis of the transformation of knowledge must begin by acknowledging language as the first and most crucial technology of reproduction.

In the contemporary understanding of the relationship between language and thought, several contentious points still require clarification, which could have a profound impact on all theories and practices related to knowledge.

The hypotheses stated above require a systematic breakdown of the process into operations, operators, and intermediate results of transformations in order to isolate the operations and transformation procedures, and to form the chain and process of creating, transforming, and using knowledge. 

In other words, before the advent of writing, the only way to transmit and preserve knowledge was through oral speech and demonstration. Language was already a carrier of information, but the methods and technologies for processing—converting sound waves (data) into content (language)—were interiorized within the human being.

The structure of working with knowledge changes once again. The key transformation is the technologization of data storage, processing, and transmission. Data virtualization occurs: an additional layer is added. The physical medium (a hard drive, a microchip) now stores not the sign itself (a letter, an image), but its digital code—a "sign of a sign", which is accessible for interpretation only by technical means. The machine performs the procedure of translating this physical data (electrical impulses) into virtual data, which is then synthesized for human recognition (an image on a screen, sound from speakers). Although the apparatus for processing virtual data into information (understanding) largely remains with the human, this virtualization opens up new possibilities. Some functions for converting data into information, for a limited set of scenarios, can be offloaded to the machine in the form of algorithms written by a human. This, combined with digital networks, unlocks a new level of speed in information dissemination.

Data transformation process in the digital age:

In this nascent era, a fundamental shift is occurring: it is no longer data that is being technologized, but information itself and, potentially, knowledge. While information systems learned to operate on data (reading, storing, transmitting), intellectual systems based on large language models (LLMs) and generative AI are beginning to perform operations that were previously the exclusive domain of human consciousness. Relational databases are being replaced by new carriers, such as vector representations, which encode not data, but the semantic proximity of concepts. This allows for fundamentally new operations on information itself.

Information transformation process in the intellectual age:

The fundamental shift is that the intellectual system, f (AI), is capable of independently transforming one piece of information into another (e.g., summarizing, translating, synthesizing) by applying context and semantic connections through its mathematical apparatus. The human, f (human), still makes the final transition to knowledge, but does so based not on raw information, but on the product of intellectual processing. This is the beginning of the technologization of thought.

The very concept of "knowledge" and the processes of working with it change with each era. As was shown in the analysis of the eras, the transition from an oral culture to a written one, and then to an informational one, fundamentally changed what was considered knowledge and how it was handled. Whereas in the pre-literate era, knowledge was inseparable from its human carrier, writing allowed it to be exteriorized, and the digital era technologized operations with data, making information globally accessible.

The processes of knowledge acquisition and accumulation also evolve from one era to the next. Each phase transition transformed the fundamental processes of working with knowledge. The oral era relied on socialization and the transmission of tacit knowledge. Writing spurred on externalization, and the information age, with its databases and networks, took the process of combining explicit knowledge to a new level.

In the intellectual era, the structure and concept of "knowledge," as well as the procedures for working with it, will change. By analogy with previous transitions, the nascent intellectual era is redefining the very essence of the cognitive process. If technologies previously automated work with data, they are now beginning to automate the transition from information to knowledge by technologizing the cognitive functions of synthesis and generalization.

The current conceptual apparatus is insufficient for designing the transition to the intellectual era. The analysis conducted in the "KNOWLEDGE" section (distinguishing between types of knowledge, the DIKW hierarchy, transformation formulas) shows that the operations and processes underlying the concept of "knowledge" are complex and multifaceted. However, they have not yet been consolidated into a single, rigorous conceptual apparatus that would allow for an adequate description and, more importantly, the design of new intellectual systems.

Fundamental questions about the nature of language remain unresolved. As was noted, language is the primary technology underlying all work with knowledge. However, key dilemmas (whether language describes the world or constructs it, what constitutes thought without language) remain open. Without a deeper understanding of these issues, any theory of the technologization of knowledge will be incomplete, and our forecasts will be limited.

The analysis presented shows that the technologies of the new intellectual era, such as large language models (LLMs) and data vectorization, are not merely tools but objects that require fundamental research. Based on the conclusions above, the following work program is formulated, aimed at establishing a solid theoretical and practical foundation for the upcoming era.